Getting Things Done, Quantified
Tiago teaches an online class which is an introduction to productivity philosophy and a method created by David Allen. Tiago needed a way for him and his students to track changes in productivity over time. He was looking for a system that had to be simple, mobile, and does not require too much time for behavior change because it’s for beginners and needs to provide real insights. In this video he shares his learnings.
HoursTracker | phone
Getting Things Done, Quantified
Good evening my name’s Tiago and as Lisa said I teach an online class which is an introduction to getting things done. I know that many of you know what getting things done is. Basically it’s a productivity philosophy and a method created by this guy, David Allen.
So over the past year I’ve introduced hundreds of people to GTD, but one question always nagged me, which is is this making a difference subjectively? So I decided that I needed a way for me and my students to track changes in productivity over time. This system had to be simple, had to be mobile, had to not require too much time for behavior change because it’s for basically beginners, and it obviously needs to provide real insights.
So I started off tracking tasks completed over a period of nine months from October of last year to June of this year. Over the same time period I tracked total hours worked much like David. Dividing the first by the second gave me a basic measure of productivity, which is task’s completed per hour which is the red line.
My data suggests that there was a 240% variation over nine months, which has huge implications if you consider that a 2% increase in your productivity gives you a whole extras week of worktime per year. So let’s look at these two types of data more closely.
To do time tracking I used an IOS app called Hours Tracker, it’s a simple manual clock in clock out system. And for tracking my tasks I used a track manager called Things, which has a Mac version and and IOS version. I would recommend Hours Tracker highly because it has very strong data export, but if I had to do it again I wouldn’t use Things just because there’s no data export which meant manually counting each completed task, not so fun.
Here you can see the Hours Tracker interface. It shows you a list of all your active projects; I had about 40. Tapping on one with two fingers logs you into that project and automatically logs you out of the previous one. Here you can see a screenshot of the logbook in Things, which is a log of every task and you can see that I can see the date it was created and the date it was completed which was the basis of my data.
So I actually identified 12 different pitfalls, 12 different weaknesses or dangers in trying to reduced something as complex as productivity to one number. I’m only going to have time to talk about three of them.
The first and probably the biggest is that not all tasks are created equal right. Some are more difficult than others. Some take more time than others. Some may take much longer but provide many times the value of something that is faster.
In each case however every time I produced three main pitfalls at least I found that there was a GTD principle that addressed it or at least minimized the pitfall. I actually found that having GTD structured my work to a degree that allowed tracking to be much easier. So, for the first pitfall GTD has a principle called the next physical action, which says that instead of writing down to-do’s as vague projects, clean garage, design website as many of us tend to do, you should reduce every task to a next physical action. So what is the specific tangible thing that you do next? So this isn’t a perfect solution, but it goes a long way towards breaking down all your different projects into tasks that are much more uniform.
The second is that not all tasks are captured in the system. Spontaneous tasks, very easy tasks, interpersonal tasks like discussing, giving feedback, talking about something. Abstract tasks like thinking, imagining, brainstorming, designing. All of these categories of tasks are very difficult to capture in the system
GTD has a principle that helps with this which is the collection habit, which is basically the idea that instead of trying to keep a list of the to-do’s in your head, the instant something occurs to you, you should write it down and capture it. So this really helps in making sure that at least you’re consistent over time and capturing everything from your brain into your system.
And third is that not all tasks are checked off right away right. I don’t know of a way to automate this. I knew that when I started this experiment that I wanted a week by week graph of changes in my productivity. But in order for that to be possible I had to make sure I was checking off each task the same week that I completed it, if I was checking off something two weeks later, my data’s completely messed up. So GTD has a practice, a habit called the weekly review.
So the weekly revue is basically a specific checklist of things that I run through at the end of every week to make sure I’m ready for the next week. And one of those items for me was, go through the to-do list and check off all completed tasks. So I know or that I’m pretty confident that at least on a weekly basis if not more often I was checking off everything I did.
I started looking for correlations between certain behaviors and productivity at the end of my experiment. I found four positive ones, all of these are counterintuitive. All of these are the opposite of very well established productivity principles.
I found that having more active projects, some more balls in the air, switching tasks more often, spending more hours working and spending more time on email were all correlated with higher productivity.
And two negative ones, which one of these is also counterintuitive. Actually focusing for more time on one thing, in other words average work session was actually negatively correlated, as was time in meetings.
Unfortunately as interesting as these are with a sample size of only 39 weeks I needed a correlation of .26 to be statistically significant. So unfortunately I can’t draw strong conclusions from these numbers. But I can draw three general insights that have been very impactful.
The first is that simplistic assumptions of productivity are not necessarily true. If email, meeting, and task switching were so damaging the productivity like everyone assumes I would have expected strong negative correlations which I did not.
The second is that small constraints can actually improve performance. So you would think having to clock in and out of jobs, for me on average eight times a day would reduce efficiency; something extra you would have to do. But I actually found that it helped me batch process my work, helped me focus on one thing at a time, and at the very least made me aware when I was switching rapidly between things.
And third which is the most simple insight but also the most important for me is that large improvements in productivity are possible. I really don’t think I would have believed a number anywhere close to 240% if I didn’t have the data. I really believe and this is why I do what I do that if more people had this kind of data about themselves they would be much more inspired, motivated to learn about productivity, which is why I believe self-tacking is the future of productivity.
I’m looking for collaborators. My next class is going to be basically QS for beginners, so please connect with me tonight or via Twitter or Email.
Thank you very much.